Knowledge Graphs (KG) play a crucial role in many modern applications (https://developers.google.com/knowledge-graph) as solutions to the information access and search problem. There have been several initiatives in the NLP [1]}, [2]}, [3]}, [4]}, and the Semantic Web [5]}, [6]}, communities suggesting an increasing trend toward adoption of KGs for scientific articles. The automatic construction of KGs from text is a challenging problem, more so owing to the multidisciplinary nature of Science at large. While machines can better handle the volume of scientific literature, they need supervisory signals to determine which elements of the text have value. The STEM-NER-60k corpus can be leveraged to construct knowledge graphs underlying fine-grained search over publications. fig:kg demonstrates an example KG that was manually annotated with relations between the entities. In the figure, the entity nodes are color-coded by their concept type: orange corresponds to process, purple for method, green corresponds to material, and blue for data.
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